Human fingerprints (FPs) are utilized as primary biometric identifiers because they are detailed, nearly unique, difficult to alter, and durable over the life of an individual. As used herein, the term “fingerprint” refers to the pattern of FP ridges (sometimes referred to as friction ridges) and associated intervening FP valleys (i.e., grooves separating adjacent FP ridges) that are formed by the skin near the tip of a person's finger (typically his/her index finger). The patterns of FP ridges and FP valleys are essentially unique for each individual, and therefore can be reliably utilized to verify the identity of an individual.
Biometric security (or biometric authentication) is used in computer science as a form of identification and access control. Biometrics refers to metrics related to human characteristics. Although fingerprint identification (aka, dactyloscopy) was originally developed to identify criminals and people who are incapacitated or deceased, an electronic form of FP identification is now widely used to provide biometric security on a wide range of computing devices, including advanced mobile phones and Internet-of-Things (IoT) motes. Biometric authentication typically involves securely storing an authorized user's FP data (i.e., data suitably descriptive of the particular FP ridge/valley pattern to distinguish the authorized user's FP from unauthorized FP from other FPs) on a device to be secured, and then controlling subsequent access to the device by requiring a prospective user to verify his/her authority by way of successfully completing an electronic FP identification process. The electronic FP identification process requires the prospective user to touch or otherwise subject his/her finger to an FP sensor connected to the biometrically secured device each time access to the biometrically secured device is attempted. The FP sensor generates “live” FP data (i.e., FP data generated by the FP sensor from the prospective user's finger at the time access is requested) that is then transmitted to the biometrically controlled device's processor for comparison with the stored FP data. When the processor verifies that the live FP data matches the stored FP data, the biometrically secured device is unlocked (i.e., made accessible to the prospective user, who has proven himself/herself to be the authorized user). If the processor fails to match the live FP data with the stored FP data, access to the biometrically secured device is denied.
There are several different types of FP sensors currently utilized to capture live FP data in biometrically secured systems, each type having certain advantages and disadvantages.
Optical FP sensors utilize light sources, optical systems and matrix photo detectors to capture and convert images of a prospective user's fingerprint into live FP data. An advantage provided by optical sensors is that they are highly accurate. A disadvantage of optical sensors is that they are complicated and expensive, and are therefore only used in advanced systems (i.e., optical FP comprise about 1% to 2% of all FP sensors). In addition, optical sensors are unable to distinguish between real and phantom fingerprints.
Capacitive FP sensors measure the impedance between the ridge and valley regions of an applied fingerprint using sensor plates formed on the surface of an integrated circuit (IC) chip. In contrast to optical sensors, capacitive FP sensors are relatively low cost, and therefore most currently-used FP sensors are of the capacitive type. A disadvantage of capacitive sensors is that their accuracy strongly depends on the ambient humidity and the properties of the prospective user's skin (i.e., when a user's skin is relatively dry, capacitive sensors are sometimes unable to accurately distinguish FP ridges from FP valleys). In addition, it is difficult to protect capacitive sensors from static electricity, which is built-up at the capacitor plates due to tribo-electric charging. The static electricity can reach hundreds of volts, and thus thick dielectric layers are used to avoid breakdowns. Even if the static electricity voltages are lower, the charged capacitors can result in vibration-related stray signals, similar to those generated by the presence of a finger, which results in parasitic or false signals. Moreover, like optical sensors, capacitive sensors cannot distinguish between real and phantom fingerprints.
Thermal FP sensors utilize thermal-type pixels configured to detect either contact by an FP ridge or non-contact by an FP valley by way of measuring minute temperature differences, and utilizing the temperature differences to generate a thermal image of an applied fingerprint. An advantage of thermal FP sensors over optical and capacitive sensors is that thermal FP sensors are believed more reliable in distinguishing between real and phantom fingerprints (i.e., because it is difficult to make phantom thermal images that accurately mimic human thermal responses and signatures).
A static-type thermal FP sensor uses an array of thermal sensors disposed below a sensor surface to generate a thermal image of a fingerprint that is pressed and maintained in a fixed (i.e., static) position against the sensor surface. A disadvantage of conventional static-type thermal sensors is that thermal equilibrium is quickly reached at the sensor surface (i.e., the temperature over pixels aligned with either FP ridges or FP valleys stabilizes to a common temperature in less than 100 milliseconds), whereby the thermal image quickly disappears soon after a finger is applied to the sensor surface. The rapid thermal stabilization makes precise timing of the sensing procedure extremely critical, thereby requiring static-type thermal FP sensors to exhibit extremely fast sensor response times and system data acquisition rates, which in turn makes it difficult for static-type thermal FP sensors to obtain accurate live FP data at low cost.
Thermistor-type thermal FP sensors utilize arrays of microthermistors comprising bolometer-type materials (e.g., vanadium oxide (VO2)) fabricated on a substrate. An advantage of thermistor-type thermal FP sensors is that they can register the entire fingerprint area in parallel (i.e., using a static press-and-hold technique instead of a swiping motion) while avoiding the rapid thermal stabilization problem mentioned above. An exemplary thermistor-type thermal FP sensor is taught in U.S. Pat. No. 6,633,656 (Pickard, 2003). As mentioned above, a disadvantage is in that that thermal equilibrium is quickly reached at the sensor surface Another thermal FP sensor approach includes a matrix of micro heating elements formed on 5 μm SOI at a pitch of 80 μm, and operates by heating the elements to tens of degrees above room temperature, where heating rates of the heating elements indicate contact with either an FP ridge or an FP valley. An exemplary micro-heater-type thermal FP sensor is taught in “Thermal Analysis of Fingerprint Sensor Having a Microheater Array”, Han et. al., Nagoya University, 1999, Int. Symposium on Micromechatronics and Human Sciences). This does not exclude the mentioned above disadvantage of fast thermal equilibrium.
Swipe-type thermal FP sensors also utilize an array of thermal sensors, but avoid the rapid thermal stabilization problem by way of requiring a prospective user to swipe (i.e., press and slide) his/her finger across the sensor surface, whereby a dynamic thermal image or multiple “snapshot” thermal images of the applied fingerprint may be captured and then combined or otherwise processed to generate live FP data. One conventional swipe-type thermal FP sensor is the FingerChip IC (product number AT77C104B) produced by Atmel Corporation of San Jose, Calif., USA). Although conventional swipe-type thermal FP sensors avoid the precise measurement timing problem associated with static-type thermal FP sensors, the resulting thermal images are often distorted during the swiping process, which requires much more complex FP retrieval algorithm.
Another class of FP sensor utilizes elements produced by Micro-Electro-Mechanical System (MEMS) fabrication techniques to detect the presence/absence of FP ridges/valleys at each pixel location on a sensor surface. One type of MEMS-based FP sensor utilizes an array of pixels, each pixel including a movable electrode disposed in a MEMS cavity, to measure pressure differences applied to the sensor surface by way of measuring changes in capacitance caused by movement of the movable electrode. That is, the movable electrode in each pixel contacted by an FP ridge is actuated such that the pixel generates a signal indicating a capacitive change, and the movable electrode in each pixel aligned with an FP valley is not actuated, and thus the pixel registers zero capacitive change. The sensor has an array of approximately 50,000 pixels and a total sensor area of approximately 11 mm by 13 mm (each pixel having a diameter of about 50 μm), and the capacitance changes generated during an FP identification process are detected by CMOS sensing circuits located under the pixels and converted into digitized signal levels that are then transmitted as live FP data. Such capacitive MEMS-type FP sensors are disclosed, for example, in “Novel Surface Structure and Its Fabrication Process for MEMS Fingerprint Sensor”, Norito Sato et. al., IEEE ED 2004. A disadvantage of this type of FP sensor is a complicated algorithm is required to account for vibrations when the ridges are applied to MEMS elements during readout procedures (i.e., during sampling).
Another type of MEMS-based FP sensor uses an array of membrane (or diaphragm) switches, where each membrane switch includes a flexible conductive upper membrane disposed over and spaced apart from a fixed lower electrode, and generates a detection signal when pressed downward by an FP ridge against the lower electrode. During an FP identification process the open/closed state of each membrane switch is measured by measuring the amount of current passed through each switch. One conventional membrane-type FP sensor is taught in U.S. Pat. No. 6,889,565 (Keith et. al, 2005). Advantages of such conventional membrane-type FP sensors includes direct binarization of live FP data (i.e., there is no need for preliminary grey-scale images processing) and immunity to wet/dry fingers and static electricity (i.e., compared with capacitive-type sensors). A disadvantage of this type of FP sensor is the necessity to have a special storage electronics fast enough to register the signal of “closed states” in case of vibrations.
In addition to the specific disadvantages set forth above, a lingering problem with all of the above-mentioned FP sensor types is that they do not facilitate secure transfer of the live FP (biometric) data that must transmitted from the FP sensor to the biometric verifier (i.e., a processor configured to compare the live FP data with the stored FP “template”). That is, because the live FP data is unencrypted, it can be intercepted during transmission from the FP sensor to the processor by unauthorized entities using relatively simple and inexpensive tampering hardware. A conventional secure FP data transmission approach is provided in U.S. Pat. No. 9,053,351 (Boshra et al, “Finger sensing apparatus using image watermarking and associated methods”), which teaches an FP sensor that utilizes watermarking circuitry to encrypt live FP data generated by an array of FP sensor pixels before transmitting the FP data from the FP sensor to the processor. In this case, after transmission, decryption circuitry is utilized to remove the watermark from the encrypted FP data before an FP identification process is performed. Although this approach provides secure transmission from the FP sensor to an external system (processor), a problem with this approach is that the FP data must be first be transmitted in an unencrypted form from the sensor array to the watermarking circuitry, which increases the risk of interception by an unauthorized entity.
Summarizing the above, what is needed is a low-cost FP sensor having a large sensing area (i.e., to facilitate static-touch FP reading, which is preferable to swipe-type scanning) and high resolution (i.e., such that each pixel is small enough to discriminate between FP ridges and FP valleys), is capable of capturing FP data without the need for precise timing circuitry, is capable of functioning under a variety of humidity conditions (i.e., can read both wet and dry fingerprints), has the ability to distinguish between real and phantom fingerprints, and is capable of transmitting secure FP data to a host system/processor in a way that overcomes the above-mentioned security issues associated with prior art solutions.